1. Technical Field
The invention relates to the field of content provision services, more particularly to content selection depending on their potential interest to the service users, and still more precisely to the ranking of news feeds by this interest.
For that purpose, the present invention relates to the field of filter ranking content items, and more particularly of selection/identification and filtering and scoring content items based on speech analysis system.
2. Related Art
Known, prior art techniques of content analyzing for rating are based on analyzing context or checking popularity and feedback of users.
International application NO. WO2010120679 titled “Emotivity and vocality measurement (Blatt Eli, Brookeside, Cates Thomas)”, discloses analyzing free-form text comments provided by a user and estimate tone of the user's feedback. Such estimations may be reflected in a single numerical value which is used for determining the customer attitude toward the company.
There are patents or patent applications which disclose analyzing texts aspects such as emotivity or sentiment, in order to rank their object (service, reviewable object, company, etc.), rather than the text itself. Some of the patents or patent applications disclose the use of “part of speech tagging” of text inputs, but in order to identify these text inputs objects, rather than to compute a score for these text inputs.
There are known techniques for estimating user sentiment and emotional level.
Us patent application NO. US2013103386 titled, “Performing sentiment analysis” (Hewlett Packard) discloses method of performing sentiment analysis by identifying one or more sentences in a microblog, identifying one or more opinion words in the sentences based on an opinion lexicon.
US patent application No. US2013103385, titled “Performing sentiment analysis” (Hewlett Packard), discloses a method of performing sentiment analysis by performing a first sentiment analysis on microblogging data based on a method using an opinion lexicon. The method also includes training a classifier using training data from the first sentiment analysis.
U.S. Pat. No. 8,417,713, titled “Sentiment detection as a ranking signal for reviewable entities” (Google), discloses a method for ranking reviewable entities based on sentiment expressed about the entities. A plurality of sentiment scores associated with the plurality of review texts are generated, wherein each sentiment score for a review text indicates a sentiment directed to the entity referenced by the review text. A plurality of ranking scores for the plurality of entities are generated wherein each ranking score is based at least in part on one or more sentiment scores associated with one or more review texts referencing the entity.
International application No. WO201310112, titled “Methods and systems for generating corporate green score using social media sourced data and sentiment analysis” (Thomson Reuters), disclose a a News/Media Analytics System (NMAS) adapted to automatically process and “read” news stories and content from blogs, twitter, and other social media sources, represented by news/media corpus, in as close to real-time as possible. Quantitative analysis, techniques or mathematics, such as green scoring/composite module and sentiment processing module are processed to arrive at green scores, green certification, and/or model the value of financial securities, including generating a green score, green compliance certification, and a composite environmental or green index.
International application No. WO2013101809, titled “Methods and systems for generating composite index using social media sourced data and sentiment analysis” (Thomson Reuters), disclose a News/Media Analytics System (NMAS) adapted to automatically process and “read” news stories and content from blogs, twitter, and other social media sources, represented by news/media corpus, in as close to real-time as possible. Quantitative analysis, techniques or mathematics, such as green scoring/composite module and sentiment processing module are processed to arrive at green scores, green certification, and/or model the value of financial securities,
US application No. US2011225174, titled “Media value engine” (General Sentiment), disclose determining a media value associated mentions of an entity in one or more documents based on a sentiment attributed to the mentions of the entity and/or a frequency with which the entity is mentioned.
US application No. US2009216524, titled “Method and system for estimating a sentiment for an entity” (Siemens Enterprise Communications), disclose a method for estimating a sentiment conveyed by the content of information sources towards an entity. The sentiment is obtained with respect to a query context that may be specified, e. g. by specific terms or expressions, like a product or service name. A sentiment dictionary having a plurality of sentiment terms is provided, wherein each sentiment term has assigned a sentiment value, and at least one of said sentiment terms is associated to a group context. Text documents are screened for occurrences of sentiment terms that are associated to a group context corresponding to the query context. Calculating a sentiment score value is performed as a function of the occurrences of sentiment terms having a similar or same group context as the query context. The method may be carried out automatically without manual analysis of the actual semantic content of the text documents under consideration.